4 papers
FUSE: Fairness-aware Update Screening against Byzantine Exploitation in -Fair Federated Learning
Tiantong Sun, Jinlin Liu, Xingru Wei +4
Client-level fair federated learning, in the form of -fair federated learning (-FFL), agnostic federated learning, or tilted empirical risk minimization, has emerged as an at…
Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
Jinlin Liu, Wei Chen, Xiaojin Zhang
Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We test whether this design scales…
Perturbation-Induced Linearization: Constructing Unlearnable Data with Solely Linear Classifiers
Jinlin Liu, Wei Chen, Xiaojin Zhang
Collecting web data to train deep models has become increasingly common, raising concerns about unauthorized data usage. To mitigate this issue, unlearnable examples introduce impe…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…